Lead AI Engineer

Key Responsibilities:1. Customer Engagement & Requirements DiscoveryLead customer workshops to understand goals, processes, challenges, and expected outcomes.Translate business requirements into structured AI use cases with clear feasibility, ROI, and prioritization.Conduct current-state assessments covering data readiness, integration needs, compliance, and security.2. AI Solution Architecture & DesignDesign end-to-end AI solutions using: Azure OpenAI Service, Azure AI Studio, Azure Machine Learning, Azure Cognitive Services, Azure Data Platform (ADLS, Fabric, Databricks, Synapse) Vector search, embeddings, and retrieval-augmented generation (RAG)Develop architecture diagrams, data flows, integration patterns, and system component definitions.Define solution options, trade-offs, and best-fit recommendations based on business value and risk.Ensure solutions adhere to enterprise standards for scalability, performance, privacy, and governance.3. Consulting & Thought LeadershipAct as a trusted advisor to customers on AI adoption, roadmap planning, and responsible AI practices.Provide clear guidance on MLOps/LLMOps, data governance, security, and deployment strategies.Share knowledge on Azure AI capabilities, innovations, and Microsoft’s product roadmap.4. Proposal Development & Presales SupportPrepare high-quality proposals, solution decks, and value proposition statements.Respond to RFP/RFI documents with accurate, clear, and competitive solution descriptions.Conduct customer presentations, demos, POCs, and technical deep dives.Partner with sales teams to articulate solution differentiation and business value.5. Delivery Handover & Execution SupportProvide technical oversight during project initiation and design phases.Support delivery teams with clarifications, design governance, and risk mitigation.Participate in review sessions to ensure solution integrity throughout implementation.6. Reusable Assets & KnowledgeCreation Create reusable frameworks, solution accelerators, demo environments, and reference architectures.Contribute to internal playbooks, best practices, and industry-specific AI templates.Stay updated with Azure AI advancements, enterprise AI trends, and generative AI patterns.Required Skills & Experience:Technical Skills:7+ years in AI/ML, cloud architecture, or solution design; at least 3 years with Azure AI.Strong hands-on expertise with:Azure OpenAI (GPT models, embeddings, chat orchestration)Azure Machine Learning (pipelines, model registry, deployment)Cognitive Services (vision, language, speech)Azure Data services (Fabric, ADLS, Databricks)API integration, microservices, and serverless architecturesExperience designing RAG architectures, conversational agents, document intelligence, predictive models, or enterprise automation solutions.Understanding of enterprise security, identity, compliance, and responsible AI guidelines.Consulting & Presales Skills:Ability to translate technical concepts into business-friendly narratives.Strong presentation, negotiation, and client-facing communication skills.Experience in writing proposals, solution briefs, and architecture documents.Ability to guide CXO-level stakeholders and handle technical deep-dive discussions.Soft Skills:Strategic thinker with strong problem-solving and analytical abilities.Confident communicator with excellent articulation skills.Team collaborator, proactive, detail-oriented, and customer-obsessed.Preferred Qualifications:Certifications: Azure AI Engineer Associate, Azure Solutions Architect Expert, Azure Data Engineer AssociateExperience in enterprise data modernization, analytics, or cloud transformation.Exposure to domain-specific AI use cases (banking, insurance, retail, government, manufacturing). “By proceeding with the job application, you are deemed to have read and acknowledged our JobApplicant Privacy Policy and consented to us using the personal data you shared for the purpose stated in the said policy.”